Semi-supervised LC/MS alignment for differential proteomics
Bioinformatics
Guest editorial: Computational intelligence and machine learning in bioinformatics
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
Objective: Differential quantification of proteins by liquid chromatography/mass spectrometry requires the alignment of a retention time axis. The alignment automatically corrects for time changes in the liquid chromatography unit when repeating two experiments. Methods: In this paper we will show an extension of non-negative canonical correlation analysis. We introduce an adaptive scale space estimation that adapts the complexity of a monotone regression function to the density of measurements across the retention time. Furthermore, a global model selection of the scale is replaced by a local one, where we estimate the scale for each individual time axis, instead of a global parameter that holds for all time axes. Results: We show in experiments that we got a 13% gain. The performance gain is measured in the number of proteins that are detected to differ significantly in abundance for two different biological samples. Conclusion: We conclude that the adaptive scale estimation and the local model selection can outperform the global model selection which yields a more effective selection of differentially abundant proteins.